Bharati Vidyapeeth College of Engineering

A paradigm shift is occurring in the manufacturing sector globally due to the digitization of operations; however, studies specific to the maintenance aspect are scarce. The research presents challenges particular to the implementation of smart maintenance in Indian manufacturing SMEs, which is unique and helps bridge the gap. This research aims to identify and analyze the hierarchical interrelationship among critical barriers to smart maintenance implementation in Indian manufacturing Small to Medium-sized Enterprises (SMEs). This study involves a two-phase approach. In the first phase, the critical barriers of smart maintenance in Indian manufacturing SMEs are identified. A literature review, along with the opinions of Industrial and academic experts, resulted in the finalization of the barriers. In the second phase, the Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) methodologies were employed to analyze the criticality of the barriers. The driving dependence power was computed for each factor involved, which was then given as an input into the MICMAC analysis to cluster the barriers into autonomous, dependent, linkage, and independent barriers. Smart maintenance is gaining traction worldwide in the manufacturing sector, driven by the emergence and popularity of Industry 4.0. The study identifies the critical hindrances to the adoption of innovative practices specific to the maintenance function of Indian SMEs. ‘Limited Interoperability of Digital Systems’ and ‘Poor Data Quality and Fragmented Data Infrastructure’ emerged as the critical barriers with high driving power. At the same time, ‘Management Resistance and Low Strategic Priority’ and ‘Lack of Cross-functional Collaboration’ appear at the top of the ISM model with high dependence power. The overall approach of smart maintenance, as presented in this research, challenges identification and structured modelling, and is expected to help managers and policymakers concentrate their efforts based on the criticality of barriers. In turn, this will facilitate a smooth transition to smart technologies in the maintenance function of Indian manufacturing SMEs.

As electric vehicles (EVs) gain popularity, enhancing battery charging systems and health management is critical for extending battery life and driving range. This research proposes an improved AC-DC power-factor-corrected resonant converter integrated with a cascaded modular multilevel three-level inverter using stacked SiC MOSFETs, an isolated LLC converter, and a front-end rectifier to optimize EV charging efficiency. To accurately predict battery State of Health (SOH), a long short-term memory (LSTM) neural network combined with an attention mechanism is employed. The LSTM model captures temporal dependencies in battery data, while the attention mechanism emphasizes the most relevant features over time, improving prediction accuracy. The model is trained on a dataset with six key features, including current, voltage, and temperature, and optimized using a Differential Evolution algorithm for hyperparameter tuning. Simulation results demonstrate a 15

The rapid proliferation of Internet of Things (IoT) devices has introduced unprecedented connectivity but simultaneously amplified cyber-security risks.The IoT networks are vulnerable to breaches and cyber attacks because of compromised security protocols and inadequately secured IoT devices. These devices have constrain over security update and lack of encryption make them prone to cyber attacks. This paper presents a comprehensive study on developing a robust intrusion detection system for IoT networks by leveraging an ensemble machine learning approach. The methodology encompasses a complete pipeline from raw traffic capture and sophisticated feature extraction using CICFlowMeter, through meticulous data pre-processing to advanced feature selection and the implementation of diverse machine learning models. Further it presents a strategic machine learning-based IDS framework using a stack classifier incorporating optimized feature selection to enhance detection accuracy and efficiency. Modelling using the K-Best algorithm to select features and extract the top 15 most important ones and building an ensemble model are the two mainstays of this method. Classification algorithms including Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) are compared with proposed custom-built Stack Classifier by considering the ‘Ton IoT dataset’. The findings unequivocally demonstrate the superior performance of the Stack Classifier, achieving significantly higher accuracy, precision, recall, and F1-score, thereby identifying it as the optimal model for enhanced intrusion detection.

The efficiency of solar thermal systems strongly depends on the heat transfer characteristics of absorber surfaces. Conventional coatings often exhibit poor wettability, leading to uneven liquid film formation, insulating vapour layers, and reduced thermal performance. This study investigates the application of super-hydrophilic surfaces to enhance heat transfer in solar thermal collectors. A tri-layer nanostructured coating composed of Zinc Oxide (ZnO), Cerium Oxide (CeO2), and Graphene Oxide (GO) was developed using plasma treatment and chemical vapor deposition to achieve superior hydrophilicity. The coating demonstrated a contact angle of 10–13°, high surface roughness (260 nm), small particle size (12 nm), and large surface area (820 m2 g−1), promoting efficient water-film formation and reduced thermal resistance. Heat transfer measurements revealed a maximum HTC of 410 W m−2 K−1 at 2.0 L min−1, a collector efficiency of 76

The injection molding is under continuous pressure to reduce waste and enhance quality without affecting the safety. Although Industry 5.0 advocates human-centric and sustainable production, the transition to the so-called Zero-Defect production is challenging since polymers can easily act unpredictably when subjected to heat and pressure. This paper presents a Responsible, Risk-Aware, and Regulated AI (RRRAI) model. We integrated a custom-made automated part separator with a high-fidelity Digital Twin to intercept defects in real-time. We did not apply deep learning models that are black boxes and cannot be trusted by the operators, but rather the Decision Tree Regression. This is associated with injection parameters namely temperature, pressure, and cycle time directly correlated with defects and is transparent to the system. An experiment on a FANUC ROBOSHOT 100 in the field revealed that our system could cut down on daily defects out of the average of 765.8 defects to 299.8 defects $(\mathrm{p}<0.001)$. The model had the prediction accuracy of 85 percent. Importantly, we ensured that the system is risk-conscious: when the AI is not certain (between 10-80 percent) then it requests a human inspection, as opposed to making guesses. The strategy is consistent with the NIST AI Risk Management Framework, which confirms that it is possible to be responsible and automate quality control.